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E-governance is a process that aims to enhance a government's ability to simplify all the processes that may involve government, citizens, businesses, and so on. The rapid evolution of digital technologies has often created the necessity for the establishment of an e-Governance model. There is often a need for an inclusive e-governance model with integrated multiactor governance services and where a single market approach can be adopted. e-Governance often aims to minimise bureaucratic processes, while at the same time including a digital-by-default approach to public services. This aims at administrative efficiency and the reduction of bureaucratic processes. It can also improve government capabilities, and enhances trust and security, which brings confidence in governmental transactions. However, solid implementations of a distributed data sharing model within an e-governance architecture is far from a reality; hence, citizens of European countries often go through the tedious process of having their confidential information verified. This paper focuses on the sinGLe sign-on e-GovernAnce Paradigm based on a distributed file-exchange network for security, transparency, cost-effectiveness and trust (GLASS) model, which aims to ensure that a citizen can control their relationship with governmental agencies. The paper thus proposes an approach that integrates a permissioned blockchain with the InterPlanetary File System (IPFS). This method demonstrates how we may encrypt and store verifiable credentials of the GLASS ecosystem, such as academic awards, ID documents and so on, within IPFS in a secure manner and thus only allow trusted users to read a blockchain record, and obtain the encryption key. This allows for the decryption of a given verifiable credential that stored on IPFS. This paper outlines the creation of a demonstrator that proves the principles of the GLASS approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel leaderless protocol (FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures) with a low communicational complexity and which allows a set of nodes to come to a consensus on a value of a single bit. The paper makes the assumption that part of the nodes are Byzantine , and are thus controlled by an adversary who intends to either delay the consensus, or break it (this defines that at least a couple of honest nodes come to different conclusions). We prove that, nevertheless, the protocol works with high probability when its parameters are suitably chosen. Along this the paper also provides explicit estimates on the probability that the protocol finalizes in the consensus state in a given time. This protocol could be applied to reaching consensus in decentralized cryptocurrency systems. A special feature of it is that it makes use of a sequence of random numbers which are either provided by a trusted source or generated by the nodes themselves using some decentralized random number generating protocol. This increases the overall trustworthiness of the infrastructure. A core contribution of the paper is that it uses a very weak consensus to obtain a strong consensus on the value of a bit, and which can relate to the validity of a transaction. • A novel leaderless protocol with a low communicational complexity. • Protocol works with high probability when its parameters are suitably chosen. • Explicit estimate on the probability of finalizing in the consensus state. • Application in reaching consensus in decentralized cryptocurrency systems. • Relies on a common sequence of random numbers for overall trustworthiness. • Very weak consensus to obtain a strong consensus on a transaction. [ABSTRACT FROM AUTHOR]

Recent cyberattacks on critical infrastructure systems coupled with the technology-induced complexity of the system of systems have necessitated a review of existing methods of assessing critical systems security risk exposure. The question is; do existing security risk assessment methods adequately address the threats of modern critical infrastructure systems? Having examined six existing assessment frameworks, we argue, the complexities associated with modern critical infrastructure systems make existing methods insufficient to assess systems security risks exposure. From systems dynamics perspectives, this paper proposes a dynamic modelling approach as an alternative. [ABSTRACT FROM AUTHOR]

Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target's data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many crypto-ransomware detection techniques is to monitor file system activity and attempt to identify encrypted files being written to disk, often using a file's entropy as an indicator of encryption. However, often in the description of these techniques, little or no discussion is made as to why a particular entropy calculation technique is selected or any justification given as to why one technique is selected over the alternatives. The Shannon method of entropy calculation is the most commonly-used technique when it comes to file encryption identification in crypto-ransomware detection techniques. Overall, correctly encrypted data should be indistinguishable from random data, so apart from the standard mathematical entropy calculations such as Chi-Square ( χ 2 ), Shannon Entropy and Serial Correlation, the test suites used to validate the output from pseudo-random number generators would also be suited to perform this analysis. The hypothesis being that there is a fundamental difference between different entropy methods and that the best methods may be used to better detect ransomware encrypted files. The paper compares the accuracy of 53 distinct tests in being able to differentiate between encrypted data and other file types. The testing is broken down into two phases, the first phase is used to identify potential candidate tests, and a second phase where these candidates are thoroughly evaluated. To ensure that the tests were sufficiently robust, the NapierOne dataset is used. This dataset contains thousands of examples of the most commonly used file types, as well as examples of files that have been encrypted by crypto-ransomware. During the second phase of testing, 11 candidate entropy calculation techniques were tested against more than 270,000 individual files—resulting in nearly three million separate calculations. The overall accuracy of each of the individual test's ability to differentiate between files encrypted using crypto-ransomware and other file types is then evaluated and each test is compared using this metric in an attempt to identify the entropy method most suited for encrypted file identification. An investigation was also undertaken to determine if a hybrid approach, where the results of multiple tests are combined, to discover if an improvement in accuracy could be achieved. [ABSTRACT FROM AUTHOR]

We are facing a rapidly growing geriatric population (65+) that will live for multiple decades and are challenged with environmental pollution far exceeding that of previous generations. Consequently, we currently have a poor understanding of how environmental pollution will impact geriatric health distinctly from younger populations. Few toxicology studies have considered age differences with geriatric individuals. Critically, all top ten most prevalent age-related diseases are linked to metal exposures. Hexavalent chromium [Cr(VI)] is a metal of major environmental health concern that can induce aging phenotypes and neurotoxicity. However, there are many knowledge gaps for Cr(VI) neurotoxicity, including how Cr(VI) impacts behavior. To address this, we exposed male rats across three ages (3-, 7-, and 18-months old) to Cr(VI) in drinking water (0, 0.05, 0.1 mg/L) for 90 days. These levels reflect the maximum contaminant levels determined by the World Health Organization (WHO) and the U.S. Environmental Protection Agency (US EPA). Here, we report how these Cr(VI) drinking water levels impacted rat behaviors using a battery of behavior tests, including grip strength, open field assay, elevated plus maze, Y-maze, and 3-chamber assay. We observed adult rats were the most affected age group and memory assays (spatial and social) exhibited the most significant effects. Critically, the significant effects were surprising as rats should be particularly resistant to these Cr(VI) drinking water levels due to the adjustments applied in risk assessment from rodent studies to human safety, and because rats endogenously synthesize vitamin C in their livers (vitamin C is a primary reducer of Cr[VI] to Cr[III]). Our results emphasize the need to broaden the scope of toxicology research to consider multiple life stages and suggest the current regulations for Cr(VI) in drinking water need to be revisited. • We exposed male rats to Cr(VI) in drinking water at MCLs set by WHO and US EPA. • We assessed behaviors in 5 assays to determine effects across ages (3-, 7-, 18 mo). • Behavior effects were observed as early as 1 week after exposure. • Middle-aged (7-month old) rats were the most impacted age group. • Social and spatial memory were the most impacted behaviors. [ABSTRACT FROM AUTHOR]

There can be performance and vulnerability concerns with block ciphers, thus stream ciphers can used as an alternative. Although many symmetric key stream ciphers are fairly resistant to side-channel attacks, cryptographic artefacts may exist in memory. This paper identifies a significant vulnerability within OpenSSH and OpenSSL and which involves the discovery of cryptographic artefacts used within the ChaCha20 cipher. This can allow for the cracking of tunneled data using a single targeted memory extraction. With this, law enforcement agencies and/or malicious agents could use the vulnerability to take copies of the encryption keys used for each tunnelled connection. The user of a virtual machine would not be alerted to the capturing of the encryption key, as the method runs from an extraction of the running memory. Methods of mitigation include making cryptographic artefacts difficult to discover and limiting memory access. [ABSTRACT FROM AUTHOR]

The challenge of securing IT environments has reached a new complexity level as a growing number of organisations adopt cloud solutions. This trend increases the possibility of overseen attack paths in an organisation's IT infrastructure. This paper proposes a methodology for assessing the security of a Microsoft cloud tenant based on the relationships between different cloud entities through the use of graphs. This paper argues for using graph theory as an effective method to understand and uncover complex entity attack paths. To achieve this, we implemented a graph analytics platform using data from a Microsoft cloud test tenant. Methods based on graph theory proved to measurably reduce possible attack paths. Our research can support defenders who want to better understand the interrelationships of Microsoft cloud entities as well as identify and remediate possible attack paths. [ABSTRACT FROM AUTHOR]

Traditional password authentication methods have raised many issues in the past, including insecure practices, so it comes as no surprise that the evolution of authentication should arrive in the form of password-less solutions. This research aims to explore the problems that password authentication and password policies present and aims to deploy Windows Hello for Business (WHFB) on-premises. This includes creating three virtual machines (VMs) and evaluating WHFB as a password-less solution and showing how an attacker with privileged access may retrieve the end user's domain password from the computer's memory using Mimikatz and describing the possible results. The conducted research tests are in the form of two attack methods. This was feasible by the creation of three VMs operating in the following way. The first VM will act as a domain controller (DC) and certificate authority server (CA server). The second VM will act as an Active Directory Federation Service (ADFS). The third VM will act as the end-user device. The test findings research summarized that password-less authentication is far more secure than the traditional authentication method; this is evidenced throughout the author's tests. Within the first test, it was possible to retrieve the password from an enrolled device for WHFB while it was still in the second phase of the deployment. The second test was a brute-force attack on the PIN of WHFB; since WHFB has measures to prevent such attacks, the attack was unsuccessful. However, even though the retrieval of the password was successful, there are several obstacles to achieving this outcome. It was concluded that many organizations still use password authentication as their primary authentication method for accessing devices and applications. Larger organizations such as Microsoft and Google support the adoption of password-less authentication for end-users, and the current usage of password-less authentication shared by both organizations is encouraged. This usually leads organizations to adopt this new solution for their IT infrastructure. This is because it has been used and tested by millions of people and has proven to be safe. This supports the findings of increased usage and the need for password-less authentication by today's users. [ABSTRACT FROM AUTHOR]

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and improve overall organisational security. An organisation's vulnerabilities to attacks might change over time. It is utterly important to find a balance among a current threat, the potential countermeasures, their consequences and costs, and the estimation of the overall risk that this provides to the organisation. For enhancing organisational security and automation, applying threat intelligence technology is critical for detecting, classifying, analysing, and sharing new cyberattack tactics. Trusted partner organisations can then share newly identified threats to improve their defensive capabilities against unknown attacks. On this basis, organisations can help reduce the risk of a cyberattack by providing access to past and current cybersecurity events through blockchain smart contracts and the Interplanetary File System (IPFS). The suggested combination of technologies can make organisational systems more reliable and secure, improving system automation and data quality. This paper outlines a privacy-preserving mechanism for threat information sharing in a trusted way. It proposes a reliable and secure architecture for data automation, quality, and traceability based on the Hyperledger Fabric private-permissioned distributed ledger technology and the MITRE ATT&CK threat intelligence framework. This methodology can also be applied to combat intellectual property theft and industrial espionage. [ABSTRACT FROM AUTHOR]

The present world of vehicle technology is inclined to develop Electric Vehicles (EVs) with various optimized features. These vehicles need frequent charging which takes a longer time to charge up. Therefore, scheduling of vehicles in charging stations is required. Besides, the information of the EVs and its location is also stored by the charging stations and therefore creates a concern of EV privacy. Various researches are going on to solve these problems; however, an efficient privacy-preserving solution is less practiced till date. In this paper, a framework for Electric Vehicle (EV) charging is discussed. The framework uses the concept of Matching Market to identify a charging station and uses the lattice-based cryptography for secure communications. The matching market considers multiple factors to provide the best allocation of charging station and cryptography ensures security and privacy preservation. The use of lattice-based cryptographic hash SWIFFT avoids heavy computation. This usage of matching market and lattice cryptography, more specifically signcryption for EV charging framework are the highlights of the solution and add-ons to the novel features. Overall, the presented framework is efficient in terms of computation and communication cost, satisfaction ratio, slot ratio, charging latency and load balancing index. The performance metrics are compared with recent developments in this field. [ABSTRACT FROM AUTHOR]

Energy uncertainty and ecological pressures have contributed to a high volatility in energy demand and consumption. The building sector accounts for 30 to 40% of the total global energy consumption. There is a high demand for novel techniques and viable energy strategies for reducing energy consumption in this domain. Energy prediction models have the potential to play a pivotal role in optimising energy consumption. The proposed work presents a new and accurate Energy Demand Prediction (EDP) model for large buildings. This approach leverages the Random Neural Network (RNN) prediction methodology. The proposed RNN-based EDP is compared with traditional Artificial Neural Network (ANN), Support Vector Machine (SVM) and linear regression models. A large building is modelled and simulated for one year in the Integrated Environment Solutions Virtual Environment (IES-VE). Several data inputs such as air temperature, internal gain and the number of people (occupancy) are calculated from IES-VE model and provided to traditional ANN and the proposed RNN predictor. A number of test parameters such as Root Mean Square (RMSE), Normalized Root Mean Square (N-RMSE), Mean Absolute Percentage Error (MAPE) and R provide the proposed RNN model with higher accuracy over the traditional ANN, SVM and linear regression. The proposed RNN predictor provides approximately half of the error of the ANN model. The traditional ANN model gives higher error values of 2.07×, 1.83× and 2.35× for RMSE, NRMSE and MAPE, respectively as compared to the proposed RNN model. Furthermore, the error values of SVM and linear regression were also higher than the proposed EDP scheme. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework. [ABSTRACT FROM AUTHOR]

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services. [ABSTRACT FROM AUTHOR]

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset. [ABSTRACT FROM AUTHOR]

In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The financial and operational advantages of moving IT infrastructure to specialised cloud providers are clearly compelling. However, with such volumes of private and personal data being stored in cloud computing infrastructures, security concerns have risen. Motivated to monitor and analyze adversarial activities, we deploy multiple honeypots on the popular cloud providers, namely Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure, and operate them in multiple regions. Logs were collected over a period of three weeks in May 2020 and then comparatively analysed, evaluated and visualised. Our work revealed heterogeneous attackers' activity on each cloud provider, both when one considers the volume and origin of attacks, as well as the targeted services and vulnerabilities. Our results highlight the attempt of threat actors to abuse popular services, which were widely used during the COVID-19 pandemic for remote working, such as remote desktop sharing. Furthermore, the attacks seem to exit not only from countries that are commonly found to be the source of attacks, such as China, Russia and the United States, but also from uncommon ones such as Vietnam, India and Venezuela. Our results provide insights on the adversarial activity during our experiments, which can be used to inform the Situational Awareness operations of an organisation. [ABSTRACT FROM AUTHOR]

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) management systems require the adoption of effective technologies when health information is being exchanged. Current management approaches often face risks that may expose medical record storage solutions to common security attack vectors. However, healthcare-oriented blockchain solutions can provide a decentralized, anonymous and secure EHR handling approach. This paper presents PREHEALTH, a privacy-preserving EHR management solution that uses distributed ledger technology and an Identity Mixer (Idemix). The paper describes a proof-of-concept implementation that uses the Hyperledger Fabric's permissioned blockchain framework. The proposed solution is able to store patient records effectively whilst providing anonymity and unlinkability. Experimental performance evaluation results demonstrate the scheme's efficiency and feasibility for real-world scale deployment. [ABSTRACT FROM AUTHOR]

Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes. [ABSTRACT FROM AUTHOR]